Future Behavior Of System Research Articles

A direct search approach to optimization for nonlinear model predictive control

SummaryNonlinear model predictive control (NMPC) depends on performing a constrained nonlinear optimization, based on predictions of future system behavior, during a sampling interval to determine the control action to be applied to the system during the next time step. The difficulty in designing an optimization procedure to solve a constrained NMPC problem is due to the finite time horizon to which the predictive model is evaluated, the state and control actuator constraints, and sampling interval length. The resulting objective function, which is to be optimized is typically not differentiable. Although there are many commercial, shareware, and open‐source optimization packages available that can perform a nonlinear constrained optimization for most cases, there are NMPC implementations requiring embedded code or that must meet stringent timing requirements that preclude the use of off‐the‐shelf packages. In cases where the predictive model is known, such as aerodynamic or hydrodynamic systems, a direct‐search optimization algorithm can perform well enough in a real‐time environment. Direct search algorithms are simple to implement and can be made more efficient by applying differential geometric techniques to the search methodology. The typical smoothness of the equations of motion for vehicular systems allows the objective function's stationarities to be handled in a straight‐forward way. Copyright © 2013 John Wiley & Sons, Ltd.

Environmental Sustainability and Ecological Complexity: Developing an Integrated Approach to Analyse the Environment and Landscape Potentials to Promote Sustainable Development

The notion of Sustainable Development has rapidly gained considerable attention since the lastdecades of the twentieth century. It has become a locus for global paradigm shift on environment and development issues particularly after Rio Conference in 1992. The comparative analysis of Agenda 21 and major UN documents, MDG, and RIO+20 as well as some major sources are made to provide a context for the assessment of landscape and environment importance for achieving sustainability. Study showed almost half of the Agenda 21 is devoted to the Environmental problems, landscape issues and protection of nature and natural resources. Landscape ecology and ecological complexity theories have been widely used in recent decades to analyze natural systems and artificial phenomena to predict the future behavior of systems; and to provide better solutions for balanced interactions of human and living organisms at larger scales. The paper investigated landscape ecology and complexity theory potentials for better understanding landscape system and its nature as an ever changing semi-living phenomenon, which plays a key role for the life of inhabitants of the planet. Using an integrated approach and analyses, this research is to develop the ecological dimensions of landscape as framework for the contribution of landscape ecology and ecological complexity towards achieving sustainable development. Analyses led the paper postulates the new dimensions: Transformation complexity and Accumulation complexity; and reveals Ecosystem complexity and Biocomplexity to expand the current dimensions of ecological complexity, with their effects on the landscape systems, environmental sustainability and hence sustainable development.

Construction of a new BRB based model for time series forecasting

It is important to predict the future behavior of complex systems. Currently there are no effective methods to solve time series forecasting problem by using the quantitative and qualitative information. Therefore, based on belief rule base (BRB), this paper focuses on developing a new model that can deal with the problem. Although it is difficult to obtain accurately and completely quantitative information, some qualitative information can be collected and represented by a BRB. As such, a new BRB based forecasting model is proposed when the quantitative and qualitative information exist simultaneously. The performance of the proposed model depends on the structure and belief degrees of BRB simultaneously. Moreover, the structure is determined by the delay step. In order to obtain the appropriate delay step using the available information, a model selection criterion is defined according to Akaike's information criterion (AIC). Based on the proposed model selection criterion and the optimal algorithm for training the belief degrees, an algorithm for constructing the BRB based forecasting model is developed. Experimental results show that the constructed BRB based forecasting model can not only predict the time series accurately, but also has the appropriate structure.

High sensitivity of tidewater outlet glacier dynamics to shape

Abstract. Variability in tidewater outlet glacier behavior under similar external forcing has been attributed to differences in outlet shape (i.e., bed elevation and width), but this dependence has not been investigated in detail. Here we use a numerical ice flow model to show that the dynamics of tidewater outlet glaciers under external forcing are highly sensitive to width and bed topography. Our sensitivity tests indicate that for glaciers with similar discharge, the trunks of wider glaciers and those grounded over deeper basal depressions tend to be closer to flotation, so that less dynamically induced thinning results in rapid, unstable retreat following a perturbation. The lag time between the onset of the perturbation and unstable retreat varies with outlet shape, which may help explain intra-regional variability in tidewater outlet glacier behavior. Further, because the perturbation response is dependent on the thickness relative to flotation, varying the bed topography within the range of observational uncertainty can result in either stable or unstable retreat due to the same perturbation. Thus, extreme care must be taken when interpreting the future behavior of actual glacier systems using numerical ice flow models that are not accompanied by comprehensive sensitivity analyses.

Analysis of Production Process Parameters by Using Data Mining Methods

This article deals with knowledge discovery in databases (abbr. KDD) and methodology of this process. The authors give an identification of production parameters and their influence on a production process. Knowledge discovery in the production databases is minimally used for the process of planning and control. There are many problems that occur in the production process. It is important to indentify the impact of manufacturing parameters on the system for managers. New discovered knowledge from production systems will help make the right decision to fulfill the objectives. Using the KDD in the control of production systems, it can be achieved better understanding of system control and can help predict a future behavior of system. The authors formulated general knowledge for improve parameters of analyzed production process. The objectives, steps and some results of the project are presented in this article

Model reduction in model predictive control of combined water quantity and quality in open channels

Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step.In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels.

The long-term cement studies project: the UK contribution to model development and testing

AbstractThe international long-term cement studies (LCS) project aims to increase the understanding of the behaviour of cement within a radioactive waste disposal system and how hyper-alkaline leachates may interact with host rock. Such an understanding enables confident, robust and safety-relevant statements to be made concerning future system behaviour, irrespective of host rock, engineered barrier system, or waste type. The LCS project involves laboratory experiments, in situ tests and numerical modelling to address these issues. The agencies participating are Nagra (Switzerland), JAEA (Japan), the Nuclear Decommissioning Authority, Radioactive Waste Mangement Directorate (UK), Posiva (Finland) and SKB (Sweden).Project activities have included: the development of conceptual and theoretical models of cement–rock interaction; testing of numerical models against data from laboratory experiments and industrial and natural analogues of cement–rock reaction; and the synthesis and incorporation of performance assessment (PA) relevant data from analogue studies. Key threads running through these studies include an analysis of issues relating to upscaling of processes and data to the greater temporal and spatial scales relevant to PA, and investigations of modelling the changes in physical properties that accompany geochemical reaction. Here we present examples of the results from model test cases, highlighting the important issues that have arisen.

Laguerre Function’s Based Model Predictive Control of Three Phase Induction Motor

Electrical drives play a vital role in industrial and domestic applications, so these drives must be more efficient and easy to control. Conventional control methods are well established and nearly fulfill all demands of modern electrical drives but there is still a need to improve these controllers. In this paper it is proposed that the conventional field oriented controllers can be replaced by Model Predictive Controllers (MPC) which are currently being used as process controllers. These MPC controllers are preferred over conventional field oriented controllers because they can predict future behavior of system and according to that predicted value they can adjust their present input in order to minimize the error signal. The major drawback of conventional MPC is that a lot of mathematical iterations are required to predict the future behavior of system so Laguerre function’s based MPC model is used to reduce the mathematical effort and results are validated for a three phase induction motor.

A Generalized Predictive Model of Water Table Fluctuations in Anisotropic Aquifer Due to Intermittently Applied Time-Varying Recharge from Multiple Basins

Mathematical models play a key role in assessing the future behavior of a groundwater system in response to various schemes of ground water resources development such as artificial recharging and in selecting an appropriate one out of many proposed schemes for its sustainable development. This paper presents an analytical solution of groundwater flow equation for unconfined, anisotropic, 2-D rectangular aquifer under the Boussinesq approximation to predict water table fluctuations in the aquifer in response to general time-varying intermittent recharge from multiple rectangular infiltration basins of different spatial dimensions. The horizontal anisotropy incorporated in the model is such that the principal axes of the hydraulic conductivity tensor are oriented parallel to the rectangular sides of the aquifer. The time-varying recharge rate is approximated by a series of line elements of different lengths and slopes depending on the nature of variation of recharge rate. The solution is obtained by using extended finite Fourier sine transform. Application of the solution is demonstrated with the help of synthetic examples. Numerical results of the analytical solutions are verified by comparison with the results obtained from MODFLOW. Numerical results indicate significant effect of anisotropy in hydraulic conductivity on the nature of water table variation.

Modeli sistemske dinamike u resavanju upravljackih problema

The models represent the key methodological tool for management problems solving in System Dynamics (SD) as a functionalist systems methodology. Above all, it is about mathematical models, built according to appropriate feedback structures, i.e. specific elements and flows that form feedback loops. Since SD is based on the assumption that a system structure represents the behavioral key determinant, SD models provide an effective prediction of the future system behavior. The paper focuses on the modeling process in SD, as a complex, iterative process, consisting of the following phases: model conceptualization, formulation, testing and implementation. Although being extremely useful in solving numerous organizational managing problems, SD models have certain disadvantages as a consequence of their quantitative nature. Qualitative modeling and group model-building, as possible directions for further SD development, are appropriately important in SD models' deficiencies elimination.

Exploiting Heterogeneity for Energy Efficiency in Chip Multiprocessors

Heterogeneous multicores are envisioned to be a promising design paradigm to combat today's challenges of power, memory, and reliability walls that are impeding chip design using deep submicron technology. Future multicores are expected to integrate multiple different cores, including GPGPUs, custom accelerators and configurable cores. In this paper, we introduce an important dimension-technology-using which heterogeneity can be introduced in multicores to improve their energy-performance envelope. Specifically, we analyze the benefits of heterogenous technologies for processor cores and cache subsystems. We discuss two promising device candidates (Tunnel-FET and Magnetic-RAM) for introducing technological diversity in the multicores and analyze their integration in the processor and cache hierarchy in detail. Our analysis shows that introducing such a kind of heterogeneity can significantly enhance the performance and energy behavior of future multicore systems.

Emission Reduction for Legacy Natural Gas Engines by Applying Model Predictive Control

AbstractReduction of ozone and greenhouse gas related emissions are a growing technological and economical challenge in the US pipeline market. New regulations demand extensive technology upgrades or replacement of a huge number of 30-60 years old integral engines. Modern advanced control methods have been identified as one key step to improve emission reduction as well as increased reliability and stability. Current control loops on legacy gas engines are acting mainly in independent cascaded PID loops. Eventually such an independent control strategy cannot meet the demands of a 0.5 g/BHP-HR NOx limit or less. An alternative approach is to replace the independent loops by a state-based controller which simultaneously sets all the loops under consideration of the future system behavior – MIMO Model Predictive Control (MPC).

Knowledge Virtualization of Future Social System Behavior through Computer Simulation

We are now well into the decade of an era in which simulation has become a most widely used technique, extensively applied in such diverse areas as social systems and space developments. In particular, this paper is devoted to exploring the impact of computer simulation on the social sciences. This article briefly reviews the use of simulation in sociology and goes on to consider in more detail specific areas such as system dynamics, cellular automata, simulation of social networks and organizations. It concludes with a consideration of the role of simulation in constructing agent-based models based on theories or social phenomena of social systems and it shows how an Agent Based Social Simulation might be implemented.

A Case Study of the Collision-Avoidance Problem Based on Bernstein–Bézier Path Tracking for Multiple Robots with Known Constraints

In this paper a case study of a new, cooperative, collision-avoidance method for multiple, nonholonomic robots based on Bernstein–Bézier curves is given. In the presented examples the velocities and accelerations of the mobile robots are constrained and the start and the goal velocity are defined for each robot. This means that the proposed method can be used as a subroutine in a huge path-planning problem in real time, in a way to split the whole path into smaller partial paths. The reference path of each robot, from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the path lengths subjected to the distances between the robots, which should be bigger than the minimum distance defined as the safety distance, and subjected to the velocities and accelerations which should be lower than the maximum allowed for each robot. When the reference paths are defined the model-predictive trajectory tracking is used to define the control. The prediction model derived from the linearized tracking-error dynamics is used to predict future system behavior. The control law is derived from a quadratic cost function consisting of the system tracking error and the control effort. The proposed method was tested with a simulation and with a real-time experiment in which four robots were used.

Practical Postcalibration Uncertainty Analysis: Yucca Mountain, Nevada

The values of parameters in a groundwater flow model govern the precision of predictions of future system behavior. Predictive precision, thus, typically depends on an ability to infer values of system properties from historical measurements through calibration. When such data are scarce, or when their information content with respect to parameters that are most relevant to predictions of interest is weak, predictive uncertainty may be high, even if the model is "calibrated." Recent advances help recognize this condition, quantitatively evaluate predictive uncertainty, and suggest a path toward improved predictive accuracy by identifying sources of predictive uncertainty and by determining what observations will most effectively reduce this uncertainty. We demonstrate linear and nonlinear predictive error/uncertainty analyses as applied to a groundwater flow model of Yucca Mountain, Nevada, the United States' proposed site for disposal of high-level radioactive waste. Linear and nonlinear uncertainty analyses are readily implemented as an adjunct to model calibration with medium to high parameterization density. Linear analysis yields contributions made by each parameter to a prediction's uncertainty and the worth of different observations, both existing and yet-to-be-gathered, toward reducing this uncertainty. Nonlinear analysis provides more accurate characterization of the uncertainty of model predictions while yielding their (approximate) probability distribution functions. This article applies the above methods to a prediction of specific discharge and confirms the uncertainty bounds on specific discharge supplied in the Yucca Mountain Project License Application.

Robust supply chain performance via Model Predictive Control

This paper presents a novel robust Model Predictive Control (MPC) method for real-time supply chain optimization under uncertainties. This method optimizes the closed-loop economic performance of supply chain systems and addresses different sources of uncertainties located external to and within the feedback loop. The future system behavior is predicted by a closed-loop model, which includes both the open-loop system model and a controller model described by an optimization problem. The robust MPC formulation involves the solution of a constrained, bi-level stochastic optimization problem, which is transformed into a tractable problem involving a limited number of deterministic conic optimization problems solved reliably using an interior point method. The robust controller is applied to a real industrial multi-echelon supply chain optimization problem, and its performance is shown to reduce stock-outs without excessive inventories.

Fractal structures in nonlinear dynamics

In addition to the striking beauty inherent in their complex nature, fractals have become a fundamental ingredient of nonlinear dynamics and chaos theory since they were defined in the 1970s. Moreover, fractals have been detected in nature and in most fields of science, with even a certain influence in the arts. Fractal structures appear naturally in dynamical systems, in particular associated with the phase space. The analysis of these structures is especially useful for obtaining information about the future behavior of complex systems, since they provide fundamental knowledge about the relation between these systems and uncertainty and indeterminism. Dynamical systems are divided into two main groups: Hamiltonian and dissipative systems. The concepts of the attractor and basin of attraction are related to dissipative systems. In the case of open Hamiltonian systems, there are no attractors, but the analogous concepts of the exit and exit basin exist. Therefore basins formed by initial conditions can be computed in both Hamiltonian and dissipative systems, some of them being smooth and some fractal. This fact has fundamental consequences for predicting the future of the system. The existence of this deterministic unpredictability, usually known as final state sensitivity, is typical of chaotic systems, and makes deterministic systems become, in practice, random processes where only a probabilistic approach is possible. The main types of fractal basin, their nature, and the numerical and experimental techniques used to obtain them from both mathematical models and real phenomena are described here, with special attention to their ubiquity in different fields of physics.

Fuzzy descriptor systems and spectral analysis for chaotic time series prediction

Predicting future behavior of chaotic time series and systems is a challenging area in the literature of nonlinear systems. The prediction accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. In addition, the generalization property of the proposed models trained by limited observations is of great importance. In the past two decades, singular or descriptor systems and related fuzzy descriptor models have been the subjects of interest due to their many practical applications in modeling complex phenomena. In this study fuzzy descriptor models, as a more recent neurofuzzy realization of locally linear descriptor systems, which have led to the introduction of intuitive incremental learning algorithm that is called Generalized Locally Linear Model Tree algorithm, are implemented in their optimal structure to be compared with several other methods. A simple but efficient technique, based on the error indices of multiple validation sets, is used to optimize the number of neurons as well as to prevent over fitting in the incremental learning algorithms. The aim of the paper is to demonstrate the advantages of fuzzy descriptor models and to make a fair comparison between the most successful neural and neurofuzzy approaches in their best structures according to prediction accuracy, generalization, and computational complexity. The Mackey–Glass time series, Lorenz time series (as two well-known classic benchmarks), Darwin sea level pressure time series and long-term prediction of Disturbance Storm Time index, an important index of geomagnetic activity (as two natural chaotic dynamics) are used as practical examples to evaluate the power of the proposed method in long term prediction of chaotic dynamics.

Evolution of natural risk: analysing changing landslide hazard in Wellington, Aotearoa/New Zealand

This paper addresses the temporal variation of rainfall-triggered landslide hazard within the broader context of natural risk evolution. Analysis of a sequence of aerial photos covering a period of 60 years allowed the establishment of a record of landsliding for a site in the Wellington region, New Zealand. The data show one very dominant peak in the magnitude of landslide occurrence in the late 1970s, followed by a continuous decrease. Landslide hazard can be expressed by the frequency and magnitude of the landslide events, with the total surface area affected used as a surrogate for magnitude. However, the distinct decline of landslide magnitude through time from the 1980s onwards indicates that landslide hazard may change with time. This possibility is further explored by correlating potential landslide triggering storms with the magnitude of the landslide event, using the ‘Antecedent Soil Water Status’ model in combination with daily rainfall. The relation between magnitudes of rainfall and magnitudes of landslide events is found to be weak, suggesting that a given ‘Critical Water Content’ (antecedent soil water status and rainfall on the day) does not produce similar magnitudes of landsliding. Furthermore, the study shows that reactivation of previous landslides before the peak landslide occurrence of the late 1970s is low, while the situation is reversed after this peak and reactivation in the subsequent years plays a larger role. It is concluded that the pattern of landsliding cannot be explained by the pattern of rainfall and other factors are controlling the variation of landslide hazard in time. A possible explanation is a change of the geomorphological system with time, instigated by a massive period of landsliding (the late 1970s peak). Subsequent sediment exhaustion of source areas resulting from this period appears to alter the system’s subsequent reaction to an external trigger such as rainfall. The study demonstrates that landslide hazard analysis in general should not rely on the integral of the frequency–magnitude relationship only, but should include potential non-linear changes of system settings to increase the understanding of future system behaviour, and therefore hazard and risk.

A method for representing and developing process models

Scientists investigate the dynamics of complex systems with quantitative models, employing them to synthesize knowledge, to explain observations, and to forecast future system behavior. Complete specification of systems is impossible, so models must be simplified abstractions. Thus, the art of modeling involves deciding which system elements to include and determining how they should be represented. We view modeling as search through a space of candidate models that is guided by model objectives, theoretical knowledge, and empirical data. In this contribution, we introduce a method for representing process-based models that facilitates the discovery of structures that explain observed behavior. This representation casts dynamic systems as interacting sets of processes that act on entities. Using this approach, a modeler first encodes relevant ecological knowledge into a library of generic entities and processes, then instantiates these theoretical components, and finally assembles candidate models from these elements. We illustrate this methodology with a model of the Ross Sea ecosystem.